code: def _multi_meas_grad(
      res, coeffs, r0, unshifted_coeff, num_measurements
  ):
      """Compute the gradient for multiple measurements by taking the linear combination of
      the coefficients and each measurement result."""
      g = []
      if r0 is None:
          r0 = [None] * num_measurements
      for meas_idx in range(num_measurements):
          # Gather the measurement results
          meas_result = [param_result[meas_idx] for param_result in res]
          g_component = _single_meas_grad(
              meas_result, coeffs, unshifted_coeff, r0[meas_idx]
          )
          g.append(g_component)
  
      return tuple(g)
  
imports: from typing import Sequence, Callable
  from functools import partial
  import numpy
  import pennylane
  from pennylane.measurements import VarianceMP
  from pennylane import transform
  from pennylane.transforms.tape_expand import expand_invalid_trainable
  from pennylane.gradients.gradient_transform import _contract_qjac_with_cjac
  from .finite_difference import finite_diff
  from .general_shift_rules import _iterate_shift_rule, frequencies_to_period, generate_shifted_tapes, process_shifts
  from .gradient_transform import _all_zero_grad, assert_no_state_returns, assert_no_tape_batching, assert_multimeasure_not_broadcasted, choose_trainable_params, find_and_validate_gradient_methods, _no_trainable_grad, reorder_grads
label_desc: Use image node_id 6 for calling a global function with example usage: _multi_meas_grad(res, coeffs, r0, unshifted_coeff, num_measurements) and returns: tuple